Brief Overview 1

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In this session, we will use the Black Friday Data available in Kaggle to study how to make the following graphical displays.

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Graphical Displays

  • Categorical Data
    • Bar Chart
    • Pie Chart
  • Quantitative Data
    • Histogram
    • Boxplot
    • Scatterplot
    • Line

Common Arguments

Here is a list of common arguments:

  • col: a vector of colors
  • main: title for the plot
  • xlim or ylim: limits for the x or y axis
  • xlab or ylab: a label for the x axis
  • font: font used for text, 1=plain; 2=bold; 3=italic, 4=bold italic
  • font.axis: font used for axis
  • cex.axis: font size for x and y axes
  • font.lab: font for x and y labels
  • cex.lab: font size for x and y labels

Brief Overview 2

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In this session, we will use the Black Friday Data available in Kaggle to study how to make the following graphical displays.

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Graphical Displays

  • Categorical Data
    • Bar Chart
    • Pie Chart
  • Quantitative Data
    • Histogram
    • Boxplot
    • Scatterplot
    • Line

Common Arguments

Here is a list of common arguments:

  • col: a vector of colors
  • main: title for the plot
  • xlim or ylim: limits for the x or y axis
  • xlab or ylab: a label for the x axis
  • font: font used for text, 1=plain; 2=bold; 3=italic, 4=bold italic
  • font.axis: font used for axis
  • cex.axis: font size for x and y axes
  • font.lab: font for x and y labels
  • cex.lab: font size for x and y labels

Data

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First 500 Observations

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Description

In order to understand the customer purchases behavior against various products of different categories, the retail company “ABC Private Limited”, in United Kingdom, shared purchase summary of various customers for selected high volume products from last month. The data contain the following variables.

  • User_ID: User ID
  • Product_ID: Product ID
  • Gender: Sex of User
  • Age: Age in bins
  • Occupation: Occupation (Masked)
  • City_Category: Category of the City (A,B,C)
  • Stay_In_Current_City_Years: Number of years stay in current city
  • Marital_Status: Marital Status
  • Product_Category_1: Product Category (Masked)
  • Product_Category_2: Product may belongs to other category also (Masked)
  • Product_Category_3: Product may belongs to other category also (Masked)
  • Purchase: Purchase Amount
Rows: 550,068
Columns: 12
$ User_ID                    <dbl> 1000001, 1000001, 1000001, 1000001, 1000002~
$ Product_ID                 <chr> "P00069042", "P00248942", "P00087842", "P00~
$ Gender                     <chr> "F", "F", "F", "F", "M", "M", "M", "M", "M"~
$ Age                        <chr> "0-17", "0-17", "0-17", "0-17", "55+", "26-~
$ Occupation                 <dbl> 10, 10, 10, 10, 16, 15, 7, 7, 7, 20, 20, 20~
$ City_Category              <chr> "A", "A", "A", "A", "C", "A", "B", "B", "B"~
$ Stay_In_Current_City_Years <chr> "2", "2", "2", "2", "4+", "3", "2", "2", "2~
$ Marital_Status             <dbl> 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0~
$ Product_Category_1         <dbl> 3, 1, 12, 12, 8, 1, 1, 1, 1, 8, 5, 8, 8, 1,~
$ Product_Category_2         <dbl> NA, 6, NA, 14, NA, 2, 8, 15, 16, NA, 11, NA~
$ Product_Category_3         <dbl> NA, 14, NA, NA, NA, NA, 17, NA, NA, NA, NA,~
$ Purchase                   <dbl> 8370, 15200, 1422, 1057, 7969, 15227, 19215~

Bar Chart

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Bar chart is a graphical display good for the general audience. Here, we study the distribution of Age Group of the company’s customers who purchased their products on Black Friday.

Usage: barplot(height, …)

A bar chart can be horizontal or vertical. Using the argument col, we can assign a color for bars. The argument main could be used to change the title of the figure. We can use RGB color code to assign colors.

Note: The margin of a figure could be set using the par() function. The order of the setting is c(bottom, left, top, right).

Analysis

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Vertical Bar Chart

Horizontal Bar Chart

Pie Chart

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Similarly, we can use pie chart to study the distribution of the city category.

Usage: pie(height, …)

Tip: Use color palette to choose colors (Google search: color scheme generator).

Analysis

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Distribution of City Category

Histogram

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Histogram is used when we want to study the distribution of a quantitative variable. Here we study the distribution of customer purchase amount.

Usage: hist(x, …)

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Analysis

Boxplot

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Boxplot 1

Here, we talk about another graphical display that can be used to study the distribution of a quantitative variable: box and whisker plot (boxplot).

Usage: boxplot(x, …) or boxplot(formula, …)

Boxplot 2

In general, a boxplot is used When we want to compare the distributions of several quantitative variables. In the following we study the distribution of customer purchase amount among different age groups.

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Analysis of Boxplot 1

Analysis of Boxplot 2

Scatterplot

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When we want to study the relationship of two quantitative variables, a scatterplot can be used. Since this data set doesn’t have another quantitative variable, we will use the built-in data mtcars in R. Then we study the relationship of miles per gallon against the weight of vehicles.

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Analysis

Line Plot

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Data

Since the Black Friday Data are not time series data, it is not appropriate to use a line plot. In the following code chunk, we create a data frame using the forecasted highest temperatures from July 13 to July 22 in 2022 (The Weather Channel).

Analysis

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Line Chart